用生成模型模拟灵长类运动感知与小鼠皮层组织，揭示大脑如何无监督理解世界。
Unveiling Secrets of Brain Function With Generative Modeling: Motion Perception in Primates & Cortical Network Organization in Mice
- 构建分层变分自编码器，模拟灵长类视觉皮层对运动的响应机制。
- 模型在无监督下准确分离出物体运动与自我运动的因果因素。
- 发现小鼠皮层存在大量重叠功能社区，反映休息状态下的动态网络结构。
本论文包含两个主要项目，通过生成建模方法探究神经科学问题。项目一（第四章）研究神经元如何编码外部世界特征，结合赫尔姆霍兹的‘知觉即无意识推断’理论与现代生成模型（如变分自编码器，VAE），提出分层VAE模型，并测试其对灵长类视觉皮层神经元在运动刺激下的响应模拟能力。结果表明，该模型对运动的感知与灵长类大脑一致；且在无监督条件下成功识别出视网膜运动输入中的物体运动与自我运动等因果因素。结果提示，分层推断是大脑理解世界的核心机制，分层VAE可有效建模此过程。项目二（第五章）分析自发脑活动的时空结构及其反映的大脑状态（如休息）。利用同时采集的fMRI与宽视野钙成像数据，发现小鼠皮层可被分解为重叠的功能社区，约一半皮层区域属于多个社区。对比显示，基于fMRI与钙信号推断的网络存在相似性与差异性。引言部分（第一章）分为两部分：1.1至1.8介绍项目一背景，1.9至1.13介绍项目二背景。第二章回顾历史，第三章提供数学基础，第六章总结并展望未来方向。
原文摘要 · Abstract (English)
This Dissertation is comprised of two main projects, addressing questions in neuroscience through applications of generative modeling. Project #1 (Chapter 4) explores how neurons encode features of the external world. I combine Helmholtz's "Perception as Unconscious Inference" -- paralleled by modern generative models like variational autoencoders (VAE) -- with the hierarchical structure of the visual cortex. This combination leads to the development of a hierarchical VAE model, which I test for its ability to mimic neurons from the primate visual cortex in response to motion stimuli. Results show that the hierarchical VAE perceives motion similar to the primate brain. Additionally, the model identifies causal factors of retinal motion inputs, such as object- and self-motion, in a completely unsupervised manner. Collectively, these results suggest that hierarchical inference underlines the brain's understanding of the world, and hierarchical VAEs can effectively model this understanding. Project #2 (Chapter 5) investigates the spatiotemporal structure of spontaneous brain activity and its reflection of brain states like rest. Using simultaneous fMRI and wide-field Ca2+ imaging data, this project demonstrates that the mouse cortex can be decomposed into overlapping communities, with around half of the cortical regions belonging to multiple communities. Comparisons reveal similarities and differences between networks inferred from fMRI and Ca2+ signals. The introduction (Chapter 1) is divided similarly to this abstract: sections 1.1 to 1.8 provide background information about Project #1, and sections 1.9 to 1.13 are related to Project #2. Chapter 2 includes historical background, Chapter 3 provides the necessary mathematical background, and finally, Chapter 6 contains concluding remarks and future directions.
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